1. Field of the Disclosure
The invention relates to the estimation of tire friction in vehicles, particularly to the estimation of maximum friction available at a specific time. The knowledge regarding maximum friction is especially desirable in anticipatory warning and steering systems, in control of anti-lock brakes, in slip control systems, and in driving stability systems.
2. Description of Related Art
In the driving stability systems, ABS brakes and slippage control systems of vehicles, as well as for example in systems estimating or automatically controlling a safety distance, it is necessary to have as precise knowledge as possible about the friction properties of a tire, it is particularly important to know the available maximum friction force and the grip characteristics of a tire in the driving situation where the tire operates close to its maximum friction force.
The knowledge as accurate as possible about a maximum friction force and an amount of tire slippage producing the maximum friction force enables the system to work faster in a braking or slipping situation. Braking, for example, can be applied from the very beginning with an almost optimal braking force, whereby the amount of slippage for the tires in braking can be made more optimal right at the beginning compared to the situation that the measuring result regarding slippage and maximum friction force is not obtained until during the braking action. Knowledge about friction is also needed in the process of cautioning the driver about slippery conditions or increased need to maintain a safety distance. In case the driver is frequently warned unnecessarily, the benefit of warnings is questionable as false alarms are mostly just annoying. On the other hand, it can be even more hazardous if the driver learns to rely on the warning and the system fails to issue a warning about slippery conditions.
Currently, the most common method of estimating maximum friction is based on driving dynamics measuring results. The maximum friction can be estimated with reasonable reliability and accuracy as long as the tire is subjected to forces which are sufficiently high with respect to the available maximum friction potential. This is based on the fact that there is always slippage between a tire and a road surface whenever the tire has force applied to it and the tire rolls along the road surface. Knowing the slippage of a tire at various values of linear or curvilinear acceleration enables a maximum friction to be concluded on the basis of measuring results. In cornering, the lateral force and the returning moment of a tire can also be used for making conclusions regarding a maximum friction of the tire. Prior art publications dealing with friction measurement by means of driving dynamics include, for example:    Pasterkamp, Wim R., The tyre as sensor to estimate friction, Delft University Press, Delft, 1997, p. 148.
Fudaka, Yoshiki, Slip-Angle Estimation for Vehicle Stability Control, Vehicle System Dynamics, 32 (1999), pp. 375-288.    Umeno, Koji, JP20022331951    Hisaaki, Asai, JP2006082620    Naoyasu, Enomoto, JP716928
On the basis of the above-cited documents, slight braking or acceleration of a vehicle enables a maximum friction to be concluded (Hisaaki, Naoyasu). In cornering, the maximum friction can be concluded when about 30% of the friction force of a maximum friction has been applied (Pasterkamp, Fukada, Umeno). The tire can be fitted with sensors measuring strains, accelerations or forces for facilitating a measuring process. Progress in the art has been rapid and in all likelihood the estimation of tire friction advances also in the future, for example by virtue of sensors integrated in a tire, a rim or a wheel hub. Therefore, quite probably, the maximum friction can be estimated in the future with a lesser-than-before friction force and more reliably than before.
However, friction measurements conducted on the basis of driving dynamics do not provide a reliable measuring result in case the friction forces applied to a tire are slight with respect to a maximum friction, i.e. the slippage resulting from the friction force is so slight that it cannot be reliably measured at least within a reasonable time span and with a sufficient accuracy. When driving a long distance straight without accelerating or braking, a maximum friction measuring result is not obtained. This is a highly typical situation in long-distance driving.
Road friction is also estimated by measuring the surface of a road with sensors. This is a way to find out whether there is ice, water, tarmac, road salt etc. on the road surface. One example of a sensor that has been found functional is Road Eye from Sweden, which measures reflection factors of appropriately selected wavelengths from a road surface. Also useful is polarization and glare reflection information, thus enabling for example a measurement of water film properties. Employed as road sensors are also radars operating over 24 GHz and 76 GHz ranges and, in principle, it is also possible to use also lower radio or alternating current frequencies for measuring dielectric or electromagnetic properties of a road. Ultrasonic sensors can be used for measuring at least the properties of snow and a soft surface. Laser sensors enable measuring a surface profile and surface roughness. A passive infrared sensor enables measuring surface temperature.
The road surface can be classified by means of a sensor and ice and water can be identified thereby. However, all that is found out from the road surface classification result is a rough estimate about a maximum friction between tire and road. For example, the friction coefficient between ice and a tire may fluctuate within the range of 0.05 to 0.5. The friction of clean tarmac is also highly inconsistent. Thus, the information provided by a sensor cannot be directly used for concluding a maximum friction as the friction depends not only on the road but also on tires, tire pressures, and a tire's surface pressure, and for example the type of ice, surface crystals, loose snow, surface roughness, temperature or cleanliness may have a major effect on the amount of friction. Accordingly, sensor measurements only provide rough information about a probable maximum friction. The road surface classification data obtained by means of a sensor can be beneficial even without knowledge about maximum friction because, for example in full-scale braking on snow, it is advisable to allow more slipping than on tarmac or ice. Hence, on different materials, the maximum friction force results from different amounts of slippage and knowing the type of road surface may assist in selecting a more effective control algorithm for example for ABS brakes. With measurements conducted during a braking or slip controlling action, finding the most effective way of braking takes usually less time if the type of road surface or the friction properties of the road is known in advance.
Measurement of friction on the basis of driving dynamics and measurement of road surface properties have been worked on in numerous projects independently of each other. On the other hand, the sensor developers aspire towards identifying for example black ice. The identification of black ice with sensors increases safety considerably, but the road surface classification result (e.g.: dry, wet, icy), as such, is only useful as a very rough friction estimate. The driving stability system is in any case required to conduct a continuous measuring process of friction forces and it is able to calculate an estimation of maximum friction at the start of slipping and when the friction forces make up about 30% of maximum friction forces. This is perhaps why those involved in the studies of driving dynamics have not regarded road sensors as worthwhile tools in the process of friction estimation and the measuring results of road sensors have not been utilized in driving control as an aid in the estimation of maximum friction, but only to caution the driver about ice, for example. The classification result as such, without the estimation of maximum friction according to the invention, can be beneficial also as input data or a parameter of the driving stability system, for example in the process of selecting automatically the chassis setups more suitable for off-road driving or in the above-described process of identifying a snow-covered road.
Publication EP0412791 describes an arrangement for estimation of friction on basis of sensor data. Because the system itself is not measuring the friction and storing the measurement results for later use. For this reason the accuracy is dependant on the accuracy of the sensors and accuracy of the empirical friction measurements and models. Therefore, the system does not adapt to ageing of sensors and wearing of the tyre.
Publication WO2008075126 describes also a similar system, but this system either doesn't update driving dynamics measurement results into classified measurement results of the road surface, and therefore the system doesn't adapt to different tyre-vehicle pairs and for example to variation of sensors.
A sensor that is useful in a system according to the invention is presented in application SE0701102 (A).
Measurement of tyre friction and properties of different road surfaces are investigated in publication: http ://www.control.1th.se/database/publications/article.pike?artkey=jsvenPDH
Inventions made in the same project with the present invention are described in publications DE102007053256 and WO2008061852.